Biology subjects

Celotto, M.

Publications and source records attributed to Celotto, M..

10 recordsLinked to original sources

bioRxiv2026

Decomposing the modulation of interactions between neuronal populations

Identifying subpopulations of neurons that interact with each other from simultaneous recordings of populations of many neurons is key for understanding across-brain communication with cellular resolution. Recent work identified communication subspaces, which capture additive interactions between pairs of high-dimensional neural populations through a small number of source and target activity patterns. However, no current method captures how a third, potentially multivariate variable - such as behavioral state or the activity of a third population - modulates these interactions. Here we extend the communication subspace framework by parameterizing modulation as a low-rank tensor. This identifies multiplicative interaction channels (MICs), defined as triplets of source, target, and modulator activity patterns, in which the modulator pattern gates the source-target interaction. We derive MICs as a bilinear perturbation of reduced-rank regression. We develop a hierarchical fitting pipeline and provide a closed-form decomposition that quantifies whether modulation reshapes the modulator-averaged baseline interaction, recruits private dimensions of one population, or opens new interactions. In simulations, MICs reliably recover the presence and geometry of ground-truth modulation even in the high-dimensional, low-sample regime. Applying MICs to simultaneous calcium imaging of prefrontal axons and interneurons in the visual cortex revealed that behavioral state asymmetrically modulates top-down interactions, reconfiguring the patterns of prefrontal projections that interact with a stable set of visual interneuron activity patterns. By providing an efficient and compact characterization of modulatory interactions, MICs enable asking new questions about how potentially high-dimensional variables shape interactions between neural populations.

neuroscience↗

bioRxiv2026

nELAVL phosphorylation by CDKL5 regulates inter-condensates composition and communication to promote experience-dependent maturation of the visual cortex

Mutations in Cyclin-Dependent Kinase-Like 5 (CDKL5) cause CDKL5 deficiency disorder (CDD), an X-linked neurodevelopmental condition. Through a phosphoproteomic screen, we identified the neuron-specific nELAVL family of RNA-binding proteins as direct activity-dependent substrates of CDKL5. In support of this regulatory axis, single-nuclei transcriptomics of Cdkl5 knockout (KO) cortices revealed an enriched reduction in activity-dependent mRNAs. Mechanistically, we show that nELAVL proteins undergo phase separation to form biomolecular condensates, the size of which is gated by CDKL5 phosphorylation. Loss of CDKL5 leads to enlarged nELAVL condensates, which exhibit reduced binding affinity for the target mRNA Fos, resulting in its accelerated degradation. This disruption extends to inter-condensate communication: phosphodeficient nELAVL show diminished interaction with P-bodies, which themselves become enlarged in CDD mutant iNeurons. Functionally, the absence of nELAVL phosphorylation recapitulates the deficits in experience-dependent visual function observed in Cdkl5 KO mice. Our findings establish a critical molecular mechanism by which CDKL5-mediated phosphorylation governs mRNA metabolism by tuning the properties of nELAVL condensates and their communication with other biomolecular condensates, ultimately promoting experience-dependent maturation of the visual cortex.

neuroscience↗

bioRxiv2024

MINT: a toolbox for the analysis of multivariate neural information coding and transmission

Information theory has deeply influenced the conceptualization of brain information processing and is a mainstream framework for analyzing how neural networks in the brain process information to generate behavior. Information theory tools have been initially conceived and used to study how information about sensory variables is encoded by the activity of small neural populations. However, recent multivariate information theoretic advances have enabled addressing how information is exchanged across areas and used to inform behavior. Moreover, its integration with dimensionality-reduction techniques has enabled addressing information encoding and communication by the activity of large neural populations or many brain areas, as recorded by multichannel activity measurements in functional imaging and electrophysiology. Here, we provide a Multivariate Information in Neuroscience Toolbox (MINT) that combines these new methods with statistical tools for robust estimation from limited-size empirical datasets. We demonstrate the capabilities of MINT by applying it to both simulated and real neural data recorded with electrophysiology or calcium imaging, but all MINT functions are equally applicable to other brain-activity measurement modalities. We highlight the synergistic opportunities that combining its methods afford for reverse engineering of specific information processing and flow between neural populations or areas, and for discovering how information processing functions emerge from interactions between neurons or areas. MINT works on Linux, Windows and macOS operating systems, is written in MATLAB (requires MATLAB version 2018b or newer) and depends on 5 native MATLAB toolboxes. The calculation of one possible way to compute information redundancy requires the installation and compilation of C files (made available by us also as pre-compiled files). MINT is freely available at https://github.com/panzerilab/MINT with DOI 10.5281/zenodo.13998526 and operates under a GNU GPLv3 license.

neuroscience↗

bioRxiv2024

Cortical norepinephrine-astrocyte signaling critically mediates learned behavior

Updating behavior based on feedback from the environment is a crucial means by which organisms learn and develop optimal behavioral strategies1-3. Norepinephrine (NE) release from the locus coeruleus (LC) has been shown to mediate learned behaviors4-6 such that in a task with graded stimulus uncertainty and performance, a high level of NE released after an unexpected outcome causes adaptations in subsequent behavior7. Yet, how the transient activity of LC-NE neurons, lasting tens of milliseconds, alters neuronal activity and influences behavior several seconds later is unclear. Here, we show that NE released after an unexpected outcome acts directly on cortical astrocytes via 1 adrenergic (Adra1a) receptors to elicit sustained increases in intracellular calcium. Chemogenetic blockade of astrocytic calcium dynamics prevents trial-to-trial behavioral adaptation. NE stimulation of astrocytes elicits ATP release, and imaging ATP levels in the cortex reveals an increase in extracellular ATP in response to an unexpected outcome. Blocking ATP-driven signaling to neuronal adenosine A1 receptors also prevents post-reinforcement behavioral adaptation. Finally, high density neuronal recordings in prefrontal cortex reveal that a surprising outcome alters the neuronal representation of the stimulus on the subsequent trial without sustained changes in cortical activity; blocking either astrocyte calcium dynamics or A1 receptors occludes these post-reinforcement changes in single-neuron and population neuronal encoding of task variables underlying behavioral changes. Together, these data demonstrate that astrocytes play an essential role in norepinephrine-driven learned behavior: they have prolonged calcium responses to transient norepinephrine release and convey task-relevant reinforcement information across behavioral intervals, enabling selective updating of neuronal task representations to support adaptive behavior.

neuroscience↗

bioRxiv2024

Confirmation Bias through Selective Use of Evidence in Human Cortex

Decision-makers often process new evidence selectively, depending on their current beliefs about the world. We asked whether such confirmation biases result from biases in the encoding of sensory evidence in the brain, or alternatively in the utilization of encoded evidence for behavior. Human participants estimated the source of a sequence of visual-spatial evidence samples while we measured cortical population activity with magnetoencephalography. Halfway through the sequence, participants were prompted to judge the more likely source category. We find that processing of subsequent evidence depends on its consistency with the previously chosen category. Evidence encoded in parietal cortex contributes more to the estimation report when that evidence is consistent with the previous choice compared to when it contradicts that choice. Our results indicate that information contradicting pre-existing beliefs has little impact on subsequent behavior, despite being precisely encoded in the brain. This provides room for deliberative control to counteract confirmation biases.

neuroscience↗

bioRxiv2024

Sampling bias corrections for accurate neural measures of redundant, unique, and synergistic information

Shannon Information theory has long been a tool of choice to measure empirically how populations of neurons in the brain encode information about cognitive variables. Recently, Partial Information Decomposition (PID) has emerged as principled way to break down this information into components identifying not only the unique information carried by each neuron, but also whether relationships between neurons generate synergistic or redundant information. While it has been long recognized that Shannon information measures on neural activity suffer from a (mostly upward) limited sampling estimation bias, this issue has largely been ignored in the burgeoning field of PID analysis of neural activity. We used simulations to investigate the limited sampling bias of PID computed from discrete probabilities (suited to describe neural spiking activity). We found that PID suffers from a large bias that is uneven across components, with synergy by far the most biased. Using approximate analytical expansions, we found that the bias of synergy increases quadratically with the number of discrete responses of each neuron, whereas the bias of unique and redundant information increase only linearly or sub-linearly. Based on the understanding of the PID bias properties, we developed simple yet effective procedures that correct for the bias effectively, and that improve greatly the PID estimation with respect to current state-of-the-art procedures. We apply these PID bias correction procedures to datasets of 53117 pairs neurons in auditory cortex, posterior parietal cortex and hippocampus of mice performing cognitive tasks, deriving precise estimates and bounds of how synergy and redundancy vary across these brain regions.

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bioRxiv2023

Measuring stimulus-related redundant and synergistic functional connectivity with single cell resolution in auditory cortex

Measures of functional connectivity have played a central role in advancing our understanding of how information is communicated within the brain. Traditionally, these studies have focused on identifying redundant functional connectivity, which involves determining when activity is similar across different sites. However, recent research has highlighted the potential importance of also identifying synergistic connectivity--that is, connectivity that gives rise to information not contained in either site alone. Here, we measured redundant and synergistic functional connectivity with individual-neuron resolution in the primary auditory cortex of the mouse during a perceptual task. Specifically, we identified pairs of neurons that exhibited directed functional connectivity between them, as measured using Granger Causality. We then used Partial Information Decomposition to quantify the amount of redundant and synergystic information carried by these neurons about auditory stimuli. Our findings revealed that functionally connected pairs carry proportionally more redundancy and less synergy than unconnected pairs, suggesting that their functional connectivity is primarily redundant in nature. Furthermore, we observe that the proportion of redundancy is higher for correct than for incorrect behavioral choices, supporting the notion that redundant connectivity is beneficial for behavior.

neuroscience↗

bioRxiv2023

An information-theoretic quantification of the content of communication between brain regions

Quantifying the amount, content and direction of communication between brain regions is key to understanding brain function. Traditional methods to analyze brain activity based on the Wiener-Granger causality principle quantify the overall information propagated by neural activity between simultaneously recorded brain regions, but do not reveal the information flow about specific features of interest (such as sensory stimuli). Here, we develop a new information theoretic measure termed Feature-specific Information Transfer (FIT), quantifying how much information about a specific feature flows between two regions. FIT merges the Wiener-Granger causality principle with information-content specificity. We first derive FIT and prove analytically its key properties. We then illustrate and test them with simulations of neural activity, demonstrating that FIT identifies, within the total information flowing between regions, the information that is transmitted about specific features. We then analyze three neural datasets obtained with different recording methods, magneto- and electro-encephalography, and spiking activity, to demonstrate the ability of FIT to uncover the content and direction of information flow between brain regions beyond what can be discerned with traditional anaytical methods. FIT can improve our understanding of how brain regions communicate by uncovering previously hidden feature-specific information flow.

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bioRxiv2022

NIT: an open-source tool for information theoretic analysis of neural population data

Information theory provides a popular and principled framework for the analysis of neural data. It allows to uncover in an assumption-free way how neurons encode and transmit information, capturing both linear and non-linear coding mechanisms and including the information carried by interactions of any order. To facilitate its application, here we present Neuroscience Information Toolbox (NIT), a new toolbox for the accurate information theoretical analysis of neural data. NIT contains widely used tools such as limited sampling bias corrections and discretization of neural probabilities for the calculation of stimulus coding in low-dimensional representation of neural activity (e.g. Local Field Potentials or the activity of small neural population).Importantly, it adds a range of recent tools for quantifying information encoding by large populations of neurons or brain areas, for the directed transmission of information between neurons or areas, and for the calculation of Partial Information Decompositions to quantify the behavioral relevance of neural information and the synergy and redundancy among neurons and brain areas. Further, because information theoretic algorithms have been previously validated mainly with electrophysiological recordings, here we used realistic simulations and analysis of real data to study how to optimally apply information theory to the analysis of two-photon calcium imaging data, which are particularly challenging due to their lower signal-to-noise and temporal resolution. We also included algorithms (based on parametric and non-parametric copulas) to compute robustly information specifically with analog signals such as calcium traces. We provide indications on how to best process calcium imaging traces and to apply NIT depending on the type of calcium indicator, imaging frame rate and firing rate levels. In sum, NIT provides a toolbox for the comprehensive and effective information theoretic analysis of all kinds of neural data, including calcium imaging.

neuroscience↗

bioRxiv2022

Stimulus information guides the emergence of behavior related signals in primary somatosensory cortex during learning

Cortical neurons in primary sensory cortex carry not only sensory but also behavior-related information. However, it remains unclear how these types of information emerge and are integrated with one another over learning and what the relative contribution of activity in individual cells versus neuronal populations is in this process. Current evidence supports two opposing views of learning-related changes: 1) sensory information increases in primary cortex or 2) sensory information remains stable in primary cortex but its readout efficiency in association cortices increases. Here, we investigate these questions in primary sensory cortex during learning of a sensory task. Over the course of weeks, we imaged neuronal activity at different depths within layers 2 and 3 of the mouse vibrissal primary somatosensory cortex (vS1) before, during, and after training on a whisker-based object-localization task. We leveraged information theoretical analysis to quantify stimulus and behavior-related information in vS1 and estimate how much neural activity encoding sensory information is used to inform perceptual choices as sensory learning progresses. We also quantified the extent to which these types of information are supported by an individual neuron or population code. We found that, while sensory information rises progressively from the start of training, choice information is only present in the final stages of learning and is increasingly supported by a population code. Moreover, we demonstrate that not only the increase in available information, but also a more efficient readout of such information in primary sensory cortex mediate sensory learning. Together, our results highlight the importance of primary cortical neurons in perceptual learning.

neuroscience↗